FAVE Design Features & Bugs
- Focus on formants.
- Focus on point measurements.
- Tries to emulate how humans have done formant analysis.
FAVE
library(shiny)
shinyAppDir("formants/")
FAVE
- For each vowel token, the F1 and F2 estimates you could get for different LPC parameter settings constitute a candidate set.
- Choose a winner based on its multivariate distance (based on F1, F2, log(B1), log(B2)) to the Atlas of North American English’s distribution for that vowel class.
- Logic: If there is an LPC setting whch is produces a measurement close to the ANAE distribution for that vowel class, it’s probably ok.
FAVE - Once more, but Bayesian this time
- The ANAE distribution for a vowel class is the prior.
- The candidate set of potential formant estimates is the likelihood.
- The winner is the posterior.
- Like most worries about Bayesian reasoning, people worry that the prior might exert too strong an influence on the posterior.
- Fortunately, the prior’s influence here doesn’t seem to be too strong.
FAVE - Step 1
library(shiny)
shinyAppDir("fave/")
FAVE - Remeasurement
library(shiny)
shinyAppDir("remeasure/")
FAVE - Step 2
library(shiny)
shinyAppDir("fave2/")
FAVE - Results
library(shiny)
shinyAppDir("fave_results/")
FAVE - Future Directions
feature/iterremeasure
- Continue iterating through re-estimation either until it arrives at a stable distribution, or a maximum iteration is reached.
feature/bootstrap
- Try to figure out the best LPC setting based on the distribution of the candidate set alone.
- This approach would only be appropriate for vowel classes with a lot of tokens.
FAVE - Future Directions

Future Directions
- Customizable measurement point heuristics
- Customizable vowel system configurations
Future Directions
Other approaches to vowel measurement might try:
- MFCCs + Multidimensional scaling of some sort.
- Treating formants more like a probability distribution
FAVE will stick to automating “traditional” approaches.